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    Item type:Publication,
    INTERNET OF THINGS BASED PRACTICAL SMART ENVIRONMENTAL MONITORING SYSTEM FOR POULTRY FARM
    (2026-01-01)
    Anuwongpinit, Thanavit
    ;
    Manthawornsiri, Chananont
    ;
    Archevapanich, Tuanjai
    ;
    Purahong, Boonchana
    In this article, we propose developing digital innovation of smart monitoring systems in the poultry farm using the Internet of Things (IoT) technology. This work aimed to design and develop a monitoring system based on an IoT system that transforms a traditional farm that uses a manual management system to apply an IoT system for environmental monitoring in a poultry farm. The main components include a hardware component that was designed and implemented to gather data of the poultry houses. Temperature and humidity sensor nodes are applied to monitoring the environment of poultry houses. The LoRa communication module in the sensor node will forward data to the gateway. The second principal component is the cloud server for data acquisition from the gateway. The cloud will be responsible for back-end processing and a web-based dashboard displaying mechanism. This system can work as an alarm notification system using LINE notify API for the LINE application that is the most popular communication application in Thailand. The system was implemented practically in one of the poultry farms in Prachinburi province, Thailand. The results indicate that the proposed system provides significant advantages, including enhanced monitoring accuracy, reduced energy consumption, and improved real-time environmental tracking for poultry farms.
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    STRAWBERRY SEEDLING CULTIVATION WITH SMART FARM
    (2025-01-01)
    Kruekaew, Boonhatai
    ;
    Palananda, Attapon
    ;
    Kimpan, Warangkhana
    In Thailand, the main planting areas are in the upper northern region. It is difficult for strawberry to be cultivated in the central region of Thailand due to inappropriate weather conditions. Because the strawberry seedlings are delicate and sensitive to alterations in temperature and weather, they require extra care than mature plants while cultivation before planting. Therefore, modifications to strawberry seedling cultivation may result in more strawberry plantings. Consequently, one of the most beneficial choices is a smart farming. This research focuses on the cultivation of strawberry seedlings in tropical areas of Thailand using an intelligence model and Internet of Things. The prototype system focuses on automatic watering and lighting, and an environmental adaptation system that combines sensors to control water, air, and lighting. The physical characteristics data from all devices in prototype system are collected, and then analyzed using machine learning methods to automatically control the environment within the prototype system to be suitable for growing strawberry seedlings. Moreover, the real-time data will be displayed on a dashboard with various notification systems. The experimental results indicated that using machine learning models can control the suitable temperature and humidity for strawberry seedlings cultivation. The appropriate temperature and soil moisture are between 31-32 degrees Celsius and 70 percent, respectively.
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    Smart collar design to predict cow behavior
    (2020-11-04)
    Nootyaskool, Supakit
    ;
    Ounsrimung, Pimolorat
    Productivity from agriculture and farming is one major to drive Thailand's economy. Low price sensors and easy to make the Internet of Thing devices for making the data collector applied in various fields such as industry, communication, and transportation. However, smart application in agriculture does not apply widespread usage. This research proposed the detail design of the smart cow collar to use monitoring cow healthy. Data of walking and scraping of the cow sent to collect on the server and report to the user known the cow healthy by relating to the behavior. The purpose of the device helps the cow keeper reducing the monitoring of cow behaviors and it can predict illness and vital of the cow occurring in the future. For predicting part, design by the data from the gyroscopes after preparing send to Hidden Markov Models to predict cow behavior, however, this paper mainly focuses on the detail building the cow collar and creating the prototype device, then testing on the real environment.